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Gruda, Jon and Hasan, Souleiman (2019) Feeling anxious? Perceiving anxiety in tweets using machine learning. Computers in Human Behavior, 98. pp. 245-255. ISSN 0747-5632
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Abstract
This study provides a predictive measurement tool to examine perceived anxiety from a longitudinal perspective,
using a non-intrusive machine learning approach to scale human rating of anxiety in microblogs. Results suggest
that our chosen machine learning approach depicts perceived user state-anxiety fluctuations over time, as well as
mean trait anxiety. We further find a reverse relationship between perceived anxiety and outcomes such as social
engagement and popularity. Implications on the individual, organizational, and societal levels are discussed.
| Item Type: | Article |
|---|---|
| Keywords: | Anxiety; Machine learning; Twitter; Micro-blog; Health; |
| Academic Unit: | Faculty of Social Sciences > School of Business |
| Item ID: | 11261 |
| Identification Number: | 10.1016/j.chb.2019.04.020 |
| Depositing User: | Jon Gruda |
| Date Deposited: | 14 Oct 2019 16:11 |
| Journal or Publication Title: | Computers in Human Behavior |
| Publisher: | Elsevier |
| Refereed: | Yes |
| Related URLs: | |
| URI: | https://mural.maynoothuniversity.ie/id/eprint/11261 |
| Use Licence: | This item is available under a Creative Commons Attribution Non Commercial Share Alike Licence (CC BY-NC-SA). Details of this licence are available here |
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